Time Series Model Building and Forecasting on Maximum Temperature Data

Coronaviruses belong to a large family of viruses which affect the hepatic, gastrointestinal, neurological and respiratory systems. The increase in the daily number of COVID-19 conﬁrmed and deaths cases from different countries of the world has brought social, economic and political activities to a standstill, affecting individuals, government, public and private sectors. In this study, autoregressive integrated moving average (ARIMA) time series model for modeling and forecasting daily confirmed, recovered, and deaths cases of COVID-19 in Nigeria was used with data on daily cases of confirmed, recovered and deaths due to COVID-19 in Nigeria from 27/02/2020-31/07/2020 obtained from Nigeria Centre for Disease Control (NCDC) website. The data from 27/02/2020-16/07/2020 were used for model building while 15 observations from 17/07/2020-31/07/2020 were used for training and forecast evaluations. Time plots and Dickey-Fuller Generalized Least Squares unit root test were used to investigate the stationarity properties of the data. Schwarz Information Criterion (SIC) in conjunction with log likelihood were used to search for optimal ARIMA models while Mean Absolute Percentage Error (MAPE) was used for forecast evaluation. Results showed that all the study variables were differenced stationary and hence integrated of order one, I (1). ARIMA (2,1,4), ARIMA (2,1,2) and ARIMA (2,1,3) models were selected as the best candidates for modeling and forecasting the confirmed, recovered and deaths cases of COVID-19 in Nigeria respectively. The study found an approximate COVID-19 life cycle of 12 days among the infected population. The 15 days’ forecasts from ARIMA (2,1,4) and ARIMA (2,1,2) models showed increases in the daily number of confirmed and recovered cases of COVID-19 in Nigeria. The forecasts from ARIMA (2,1,3) model however showed fluctuating trend with decline in the number of deaths cases due to the disease. The result of the study further showed that improving on the present approach to treatment will further decrease the number of casualties due to COVID-19 in Nigeria.

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A Time Series Model for Estimating the Weekly Winter Maximum Temperature of Northwest Territories

Journal of Applied Meteorology ◽

10.1175/1520-0450(1995)034<0847:atsmfe>2.0.co;2 ◽

1995 ◽

Vol 34 (4) ◽

pp. 847-860 ◽

Cited By ~ 2

Author(s):

Thian Yew Gan

Keyword(s):

Time Series ◽

Northwest Territories ◽

Time Series Model ◽

Maximum Temperature

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Discounted polynomials for multiple time series model building

Biometrika ◽

10.1093/biomet/69.1.107 ◽

1982 ◽

Vol 69 (1) ◽

pp. 107-115 ◽

Cited By ~ 3

Author(s):

HELMUT LÜTKEPOHL

Keyword(s):

Time Series ◽

Model Building ◽

Time Series Model ◽

Multiple Time ◽

Multiple Time Series

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Analysis of the Spread of COVID-19 in the USA with a Spatio-Temporal Multivariate Time Series Model

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18020774 ◽

2021 ◽

Vol 18 (2) ◽

pp. 774

Author(s):

Rongxiang Rui ◽

Maozai Tian ◽

Man-Lai Tang ◽

George To-Sum Ho ◽

Chun-Ho Wu

Keyword(s):

Time Series ◽

Minimum Temperature ◽

Multivariate Time Series ◽

Time Series Model ◽

Maximum Temperature ◽

Atmospheric Ozone ◽

Temperature Minimum ◽

Risk Effect ◽

The Usa ◽

Spatio Temporal

With the rapid spread of the pandemic due to the coronavirus disease 2019 (COVID-19), the virus has already led to considerable mortality and morbidity worldwide, as well as having a severe impact on economic development. In this article, we analyze the state-level correlation between COVID-19 risk and weather/climate factors in the USA. For this purpose, we consider a spatio-temporal multivariate time series model under a hierarchical framework, which is especially suitable for envisioning the virus transmission tendency across a geographic area over time. Briefly, our model decomposes the COVID-19 risk into: (i) an autoregressive component that describes the within-state COVID-19 risk effect; (ii) a spatiotemporal component that describes the across-state COVID-19 risk effect; (iii) an exogenous component that includes other factors (e.g., weather/climate) that could envision future epidemic development risk; and (iv) an endemic component that captures the function of time and other predictors mainly for individual states. Our results indicate that maximum temperature, minimum temperature, humidity, the percentage of cloud coverage, and the columnar density of total atmospheric ozone have a strong association with the COVID-19 pandemic in many states. In particular, the maximum temperature, minimum temperature, and the columnar density of total atmospheric ozone demonstrate statistically significant associations with the tendency of COVID-19 spreading in almost all states. Furthermore, our results from transmission tendency analysis suggest that the community-level transmission has been relatively mitigated in the USA, and the daily confirmed cases within a state are predominated by the earlier daily confirmed cases within that state compared to other factors, which implies that states such as Texas, California, and Florida with a large number of confirmed cases still need strategies like stay-at-home orders to prevent another outbreak.

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